Análise de um sistema de recomendação de restaurantes sensível ao contexto sobre o grau de satisfação dos usuários

Abstract

Popular applications of recommender systems can be found in many areas. In the food business, platforms such as TripAdvisor stand out for suggesting specialized restaurant recommendations based on various types of relevant information, such as reviews from other users for the menu, atmosphere and recommendations for the closest restaurants are some of the specialties of these platforms. With the possibility of using new data sensitive to the user’s context, the main objective of this work is to evaluate the usage of the reason of going to the restaurant to reorganize the final restaurants recommendation through a context-based post-filtering. To achieve the goal, a mobile application was developed, the SR Recife Restaurants, to assess the degree of satisfaction of real users to the recommended restaurants, an online evaluation approach, using questionnaires, was used. When carrying out the experiment with 15 users, it was possible to notice an increase of 26.67% in the degree of satisfaction of the top-5 first recommendations when using the trip type to the restaurant as context data for the post-filtering phase.